From Data Quality to Intelligent Automation: A Three-stage Analytics Maturity Framework for Fashion Retail

Abstract

Fashion retailers face a critical sequencing dilemma in their digital transformation journey, with 87% of analytics efforts failing to bridge technical investment to concrete business results. This paper addresses this analytics value gap by introducing a three-stage evolution model that allows fashion retailers to build analytics capability systematically to maximize business impact. Based on marketplace data including 5,381 fashion items and analyzed through ROI calculation and machine-learning comparison, we show that the data quality foundation (Stage 1) generates exceptionally high returns, at a ratio of 17.5:1 in ROI. The returns in subsequent stages are lower with 8.0:1 in Stage 2 and 4.5:1 in Stage 3. Our modeling experiment reveals that sequential progression through maturity stages is critical, as skipping stages leads to performance degradation in forecasting accuracy. The framework provides fashion executives with an evidence-based roadmap for digital transformation, emphasizing sequential capability building and strategic resource allocation. Results also reveal that premium prices correlate positively with social engagement, while discounts show no strong association with product ratings, questioning standard markdown practices. We provide implementation guidance with clear transition criteria to support fashion retailers in evolving from analytics adoption to intelligence leadership.